The Diet-Aware Dining Table: Observing Dietary Behaviors over a Tabletop Surface

Chang, Keh‐Minn; Liu, Shih-Yen; Chu, Hao-Hua; Hsu, Jane Yung-jen; Chen, Cheryl; Lin, Tung-Yun; Chen, Chieh-Yu; Huang, Polly

doi:10.1007/11748625_23

Cited by 94 publications

(71 citation statements)

References 9 publications

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“…Some of these technologies focus on assurance, or making sure our friends and loved ones are safe and healthy at home. AmI techniques for recognizing activities [18,118,122], monitoring diet and exercise [53,69], and detecting changes or anomalies [37] support this goal.…”

Section: Health Monitoring and Assistancementioning

confidence: 99%

Ambient intelligence: Technologies, applications, and opportunities

Cook

Augusto

Jakkula

2009

Pervasive and Mobile Computing

825

443

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Section: Health Monitoring and Assistancementioning

confidence: 99%

Ambient intelligence: Technologies, applications, and opportunities

Cook

Augusto

Jakkula

2009

Pervasive and Mobile Computing

825

443

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“…Chang et al [12] developed a monitoring table to detect activities in a dining scenario. The table is partitioned into several sensing sections equipped with radio-frequency-identification (RFID) readers to identify food containers and weight sensors to track food transport between containers and personal plates.…”

Section: Dietary Activity Domains and Related Workmentioning

confidence: 99%

Recognition of dietary activity events using on-body sensors

Amft

Tröster

2008

Artificial Intelligence in Medicine

195

148

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SummaryObjective: An imbalanced diet elevates health risks for many chronic diseases including obesity. Dietary monitoring could contribute vital information to lifestyle coaching and diet management, however, current monitoring solutions are not feasible for a long-term implementation. Towards automatic dietary monitoring, this work targets the continuous recognition of dietary activities using on-body sensors. Methods: An on-body sensing approach was chosen, based on three core activities during intake: arm movements, chewing and swallowing. In three independent evaluation studies the continuous recognition of activity events was investigated and the precision-recall performance analysed. An event recognition procedure was deployed, that addresses multiple challenges of continuous activity recognition, including the dynamic adaptability for variable-length activities and flexible deployment by supporting one to many independent classes. The approach uses a sensitive activity event search followed by a selective refinement of the detection using different information fusion schemes. The method is simple and modular in design and implementation. Results: The recognition procedure was successfully adapted to the investigated dietary activities. Four intake gesture categories from arm movements and two food groups from chewing cycle sounds were detected and identified with a recall of 80-90% and a precision of 50-64%. The detection of individual swallows resulted in 68% recall and 20% precision. Sample-accurate recognition rates were 79% for movements, 86% for chewing and 70% for swallowing. Conclusions: Body movements and chewing sounds can be accurately identified using on-body sensors, demonstrating the feasibility of on-body dietary monitoring. Further investigations are needed to improve the swallowing spotting performance. #

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“…Among many applications of activity recognition, a special interest is in the pervasive e-Health domain where automatic activity recognition is used in rehabilitation systems, chronic disease management, monitoring of the elderly, as well as in personal well being applications (see, e.g., [6,26,2]). …”

Section: Introductionmentioning

confidence: 99%

COSAR: hybrid reasoning for context-aware activity recognition

Riboni

Bettini

2010

Pers Ubiquit Comput

247

159

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Human activity recognition is a challenging problem for context-aware systems and applications. Research in this field has mainly adopted techniques based on supervised learning algorithms, but these systems suffer from scalability issues with respect to the number of considered activities and contextual data. In this paper, we propose a solution based on the use of ontologies and ontological reasoning combined with statistical inferencing. Structured symbolic knowledge about the environment surrounding the user allows the recognition system to infer which activities among the candidates identified by statistical methods are more likely to be the actual activity that the user is performing. Ontological reasoning is also integrated with statistical methods to recognize complex activities that cannot be derived by statistical methods alone. The effectiveness of the proposed technique is supported by experiments with a complete implementation of the system using commercially available sensors and an Android-based handheld device as the host for the main activity recognition module.

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The Diet-Aware Dining Table: Observing Dietary Behaviors over a Tabletop Surface

Cited by 94 publications

References 9 publications

Ambient intelligence: Technologies, applications, and opportunities

Ambient intelligence: Technologies, applications, and opportunities

Recognition of dietary activity events using on-body sensors

COSAR: hybrid reasoning for context-aware activity recognition

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